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Found 44 papers, 25 papers with code
Q^{2}: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering
Neural knowledge-grounded generative models for dialogue often produce content that is factually inconsistent with the knowledge they rely on, making them unreliable and limiting their applicability.
Abstractive Text Summarization
Natural Language Inference
+3
Active Learning for BERT: An Empirical Study
Here, we present a large-scale empirical study on active learning techniques for BERT-based classification, addressing a diverse set of AL strategies and datasets.
MuLER: Detailed and Scalable Reference-based Evaluation
For example, nouns and verbs are among the most frequent POS tags.
Jump to Conclusions: Short-Cutting Transformers With Linear Transformations
Moreover, in the context of language modeling, our method allows "peeking" into early layer representations of GPT-2 and BERT, showing that often LMs already predict the final output in early layers.
Knowledge is a Region in Weight Space for Fine-tuned Language Models
Specifically, we show that starting from the center of the region is as good or better than the pre-trained model in 11 of 12 datasets and improves accuracy by 3. 06 on average.
Call for Papers -- The BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus
In partnership with CoNLL and CMCL, we provide a platform for approaches to pretraining with a limited-size corpus sourced from data inspired by the input to children.
ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning
We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets.
DisentQA: Disentangling Parametric and Contextual Knowledge with Counterfactual Question Answering
Question answering models commonly have access to two sources of "knowledge" during inference time: (1) parametric knowledge - the factual knowledge encoded in the model weights, and (2) contextual knowledge - external knowledge (e. g., a Wikipedia passage) given to the model to generate a grounded answer.
Where to start? Analyzing the potential value of intermediate models
Such a model, finetuned on some source dataset, may provide a better starting point for a new finetuning process on a desired target dataset.
Reinforcement Learning with Large Action Spaces for Neural Machine Translation
Applying Reinforcement learning (RL) following maximum likelihood estimation (MLE) pre-training is a versatile method for enhancing neural machine translation (NMT) performance.
Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours
Text classification can be useful in many real-world scenarios, saving a lot of time for end users.
PreQuEL: Quality Estimation of Machine Translation Outputs in Advance
We show that this augmentation method can improve the performance of the Quality-Estimation task as well.
Some Grammatical Errors are Frequent, Others are Important
In Grammatical Error Correction, systems are evaluated by the number of errors they correct.
Fusing finetuned models for better pretraining
We also show that fusing is often better than intertraining.
Cluster & Tune: Boost Cold Start Performance in Text Classification
In real-world scenarios, a text classification task often begins with a cold start, when labeled data is scarce.
Semantics-aware Attention Improves Neural Machine Translation
We further show an additional gain when using both semantic and syntactic structures in some language pairs.
On Neurons Invariant to Sentence Structural Changes in Neural Machine Translation
We present a methodology that explores how sentence structure is reflected in neural representations of machine translation systems.
The Grammar-Learning Trajectories of Neural Language Models
These findings suggest that there is some mutual inductive bias that underlies these models' learning of linguistic phenomena.
ComSum: Commit Messages Summarization and Meaning Preservation
We present ComSum, a data set of 7 million commit messages for text summarization.
Part of Speech and Universal Dependency effects on English Arabic Machine Translation
In this research paper, I will elaborate on a method to evaluate machine translation models based on their performance on underlying syntactical phenomena between English and Arabic languages.
$Q^{2}$: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering
Neural knowledge-grounded generative models for dialogue often produce content that is factually inconsistent with the knowledge they rely on, making them unreliable and limiting their applicability.
Mediators in Determining what Processing BERT Performs First
Probing neural models for the ability to perform downstream tasks using their activation patterns is often used to localize what parts of the network specialize in performing what tasks.
GrASP: A Library for Extracting and Exploring Human-Interpretable Textual Patterns
Data exploration is an important step of every data science and machine learning project, including those involving textual data.
SERRANT: a syntactic classifier for English Grammatical Error Types
SERRANT is a system and code for automatic classification of English grammatical errors that combines SErCl and ERRANT.
Enhancing the Transformer Decoder with Transition-based Syntax
Notwithstanding recent advances, syntactic generalization remains a challenge for text decoders.
Cluster & Tune: Enhance BERT Performance in Low Resource Text Classification
In such low resources scenarios, we suggest performing an unsupervised classification task prior to fine-tuning on the target task.
Classifying Syntactic Errors in Learner Language
We present a method for classifying syntactic errors in learner language, namely errors whose correction alters the morphosyntactic structure of a sentence.
Unsupervised Expressive Rules Provide Explainability and Assist Human Experts Grasping New Domains
Approaching new data can be quite deterrent; you do not know how your categories of interest are realized in it, commonly, there is no labeled data at hand, and the performance of domain adaptation methods is unsatisfactory.
Corpus Wide Argument Mining -- a Working Solution
One of the main tasks in argument mining is the retrieval of argumentative content pertaining to a given topic.
Automatically Extracting Challenge Sets for Non-Local Phenomena in Neural Machine Translation
We show that the state-of-the-art Transformer MT model is not biased towards monotonic reordering (unlike previous recurrent neural network models), but that nevertheless, long-distance dependencies remain a challenge for the model.
Automatically Extracting Challenge Sets for Non local Phenomena in Neural Machine Translation
We show that the state of the art Transformer Machine Translation (MT) model is not biased towards monotonic reordering (unlike previous recurrent neural network models), but that nevertheless, long-distance dependencies remain a challenge for the model.
Are You Convinced? Choosing the More Convincing Evidence with a Siamese Network
With the advancement in argument detection, we suggest to pay more attention to the challenging task of identifying the more convincing arguments.
On the Weaknesses of Reinforcement Learning for Neural Machine Translation
Reinforcement learning (RL) is frequently used to increase performance in text generation tasks, including machine translation (MT), notably through the use of Minimum Risk Training (MRT) and Generative Adversarial Networks (GAN).
Learning to combine Grammatical Error Corrections
We also present a spellchecker created for this task which outperforms standard spellcheckers tested on the task of spellchecking.
Ranked #4 on
Grammatical Error Correction
on BEA-2019 (test)
Let's Agree to Agree: Neural Networks Share Classification Order on Real Datasets
We further show that this pattern of results reflects the interplay between the way neural networks learn benchmark datasets.
The Language of Legal and Illegal Activity on the Darknet
The non-indexed parts of the Internet (the Darknet) have become a haven for both legal and illegal anonymous activity.
SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA
We present the SemEval 2019 shared task on UCCA parsing in English, German and French, and discuss the participating systems and results.
Inherent Biases in Reference-based Evaluation for Grammatical Error Correction
The prevalent use of too few references for evaluating text-to-text generation is known to bias estimates of their quality (henceforth, low coverage bias or LCB).
Will it Blend? Blending Weak and Strong Labeled Data in a Neural Network for Argumentation Mining
We propose a methodology to blend high quality but scarce strong labeled data with noisy but abundant weak labeled data during the training of neural networks.
SemEval 2019 Shared Task: Cross-lingual Semantic Parsing with UCCA - Call for Participation
Given the success of recent semantic parsing shared tasks (on SDP and AMR), we expect the task to have a significant contribution to the advancement of UCCA parsing in particular, and semantic parsing in general.
Automatic Metric Validation for Grammatical Error Correction
Metric validation in Grammatical Error Correction (GEC) is currently done by observing the correlation between human and metric-induced rankings.
Inherent Biases in Reference based Evaluation for Grammatical Error Correction and Text Simplification
The prevalent use of too few references for evaluating text-to-text generation is known to bias estimates of their quality ({\it low coverage bias} or LCB).
DORA The Explorer: Directed Outreaching Reinforcement Action-Selection
We compare our approach to commonly used RL techniques, and show that using $E$-values improves learning and performance over traditional counters.
Reference-less Measure of Faithfulness for Grammatical Error Correction
We propose USim, a semantic measure for Grammatical Error Correction (GEC) that measures the semantic faithfulness of the output to the source, thereby complementing existing reference-less measures (RLMs) for measuring the output's grammaticality.
